Introduction to the TPLP special issue, logic programming in databases: From Datalog to semantic-web rules

Much has happened in data and knowledge base research since the introduction of the relational model in Codd (1970) and its strong logical foundations influence its advances ever since. Logic has been a common ground where Database and Artificial Intelligence research competed and collaborated with each other for a long time (Abiteboul et al. 1995). The product of this joint effort has been a set of logic-based formalisms, such as the Relational Calculus (Codd 1970), Datalog (Ceri et al. 1990), Description Logics (Baader et al. 2007), etc., capturing not only the structure but also the semantics of data in an explicit way, thus enabling complex inference procedures.This special issue contains three rigorously reviewed articles addressing problems that span from Query Answering to Data Mining. All these contributions have their roots in the foundational formalisms of Data and Knowledge Bases such as Logic Programming, Description Logic and Hybrid Logics, representing a clear example of the effort that the Database and the Semantic-Web communities are producing to bridge the various schools of thinking in modern Data and Knowledge Management

Pushing context-awareness down to the core: moreflexibility for the PerLa language

Information technology is increasingly pervading our envi- ronment, making real Mark Weiser’s vision of a “disappear- ing technology”. The work described in this paper focuses on using context to enable pervasive system personaliza- tion, allowing context-aware sensor-data tailoring. Since sensor networks, besides data collection, are also able to pro- duce active behaviours, the tailoring capabilities are also ex- tended to these, thus applying context-awareness to generic system operations. Moreover, because the number of pos- sible context can grow rapidly with the complexity of the application, the design phase is also supported by the possi- bility to speed-up and modularize the definition of the data and operations associated with each specific context, pro- ducing a support tool that eases the job of the designers of modern context-aware pervasive systems

Towards autonomic pervasive systems: the PerLa context language

The property of context-awareness, inherent to a Pervasive System, requires a clear definition of context and of how the context parameter values must be extracted from the real world. Since often the same variables are common to the operational system and to the context it operates into, the usage of the same language to manage both the application and the context can lead to substantial savings in application development time and costs. In this paper we propose a context-management extension to the PerLa language and middleware that allows for declarative gathering of context data from the environment, feeding this data to the internal context model and, once a context is active, acting on the relevant resources of the pervasive system, according to the chosen contextual policy

A synopsis based approach for XML fast approximate querying

In the last few years, XML has spread in many application fields and today it is used as a format to exchange data on the web, to ensure inter-operability among applications. Due to this success, the W3C has proposed a new query language, XQuery [25], specifically designed to query XML data. XQuery is a well-defined but rather complex language [14]. In this work we propose a new approach to overcome the problem of the high computational costs required by aggregate queries over massive XML data collections. In traditional relational warehouses [11] a similar problem is solved by means of fast approximate queries, that use concise data statistics based on histograms or on other statistical techniques. Their most common application is for aggregate queries in modern decision support systems, where large volumes of data need to be queried, and quick and interactive responses from the DBMS are claimed, e.g., to analyze the data in the warehouse in order to get trend information to evaluate marketing strategies. In such applications, users are often more interested to obtain an approximate answer computed in a short time rather than an exact one obtained in some minutes or, at the worst, hours

Decrease in neutrophil-to-lymphocyte ratio during neoadjuvant chemotherapy as a predictive and prognostic marker in advanced ovarian cancer

Since chronic inflammation is associated with ovarian cancer growth and progression, some clinical studies have assessed the association between the pre-treatment neutrophil-to-lymphocyte ratio (NLR) and the prognosis of ovarian cancer. The purpose of this study was to assess the dynamic behavior of the NLR during the course of neoadjuvant chemotherapy (NACT) in patients with high grade serous (HGS) advanced epithelial ovarian cancer and assess its correlation with clinical response, progression free survival (PFS) and changes in other inflammatory indexes. We performed a prospective observational study on 161 patients who underwent NACT at the Department of Gynecologic Oncology, ARNAS G. Brotzu, Cagliari, between 2009 and 2019. NLR was evaluated before starting and after three cycles of NACT. Based on response after three cycles of NACT, patients were divided into two groups: responsive and non-responsive. The primary endpoint was to assess the predictive role of NLR by comparing the responsive and non-responsive patients at baseline and after three cycles of NACT. Secondary endpoints were (a) to correlate NLR with other inflammation markers (CRP, fibrinogen, ferritin, IL-6), albumin, and modified Glasgow Prognostic Score (mGPS) with NLR at baseline and after NACT; (b) to assess the association between NLR and PFS. We found that the NLR value at baseline was not associated with response to NACT, while a decrease in NLR after three cycles was correlated with a better response to NACT. Also, values of CRP, IL-6, ferritin, and mGPS after three cycles of NACT (but not at baseline) were significantly associated with clinical response. Moreover, we found that patients with a low NLR value after 3 cycles of NACT, but not at baseline, had a significantly higher PFS than patients with high NLR after 3 cycles of NACT. In conclusion, NLR change during treatment could serve as a predictive marker of response to NACT in patients with HGS advanced ovarian cancer. This allows for the early identification of non-responsive patients who will need treatment remodeling